Evaluations also include quantitative analysis comparing performance of the-state-of-art methods for the task of video-level emotion understanding. Here, three RGB-based models with good performance on MG recognition, namely I3D [1]}, TRN [2]}, and TSM [3]} are selected for comparison. Similarly, two pose-based methods ST-GCN [4]} and MS-G3D [5]} are chosen. Those models follow the same configurations with the clip-level recognition. For example, in the task of clip-level MG recognition, TSM [3]} divides a clip (input) into 8 segments and samples one RGB frame from each segment to form the input. Now, in the video-level emotion understanding, only the input is changed to a video. We report the performances of these networks in Table REF , we can see the TSM [3]} and TRN [2]} only obtain an emotion classification accuracy of 53 percentage by using the RGB frames as input. The ST-GCN [4]} and MS-G3D [5]} yield the similar results with the pose data as input. It is noted that I3D [1]} achieves the best score among those models. I3D concatenates outputs from multi-parallel branches at the same level but with different resolutions, which can provide richer representations in emotion classification.
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